app.py

import streamlit as st
import cv2
import numpy as np
from PIL import Image
import torch
import matplotlib.pyplot as plt
from transformers import OwlViTProcessor, OwlViTForObjectDetection
from transformers.image_utils import ImageFeatureExtractionMixin


st.set_option('deprecation.showfileUploaderEncoding', False)

model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")
processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
device = torch.device("cpu")
model = model.to(device)
model.eval()

st.title('Zero-shot Object Detection')

# Input image and query image upload
col1, col2 = st.columns(2)
with col1:
    uploaded_image = st.file_uploader("Upload input image(image to predict)", type=["jpg", "jpeg", "png"])
    if uploaded_image is not None:
        image = Image.open(uploaded_image)
        st.image(image, caption='Input Image', use_column_width=True)
    #else:
        #st.image('2.png', caption='Input Image', use_column_width=True)
with col2:
    uploaded_query = st.file_uploader("Upload query image(image contains object we wanna predict)", type=["jpg", "jpeg", "png"])
    if uploaded_query is not None:
        query_image = Image.open(uploaded_query)
        st.image(query_image, caption='Query Image', use_column_width=True)
    #else:
        #st.image('1.png', caption='Input Image', use_column_width=True)

# Threshold ratio bar and class name input
threshold_ratio = st.slider('Select threshold ratio:', min_value=0.0, max_value=1.0, step=0.1, value=0.6)
#class_name = st.text_input('Enter class name:', value='agumon')
# 
start_button = st.button('Start prediction')
if uploaded_image is not None and uploaded_query is not None and start_button:
    # Process input and query image
    target_sizes = torch.Tensor([image.size[::-1]])
    inputs = processor(images=image, query_images=query_image, return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = model.image_guided_detection(**inputs)

    img = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
    outputs.logits = outputs.logits.cpu()
    outputs.target_pred_boxes = outputs.target_pred_boxes.cpu() 

    results = processor.post_process_image_guided_detection(outputs=outputs, threshold=threshold_ratio, nms_threshold=0.3, target_sizes=target_sizes)
    boxes, scores = results[0]["boxes"], results[0]["scores"]

    # Draw predicted bounding boxes and text
    for box, score in zip(boxes, scores):
        box = [int(i) for i in box.tolist()]
        cx,cy,x,y=box
        img = cv2.rectangle(img, box[:2], box[2:], (255,0,0), 5)
        if box[3] + 25 > 768:
            y = box[3] - 10
        else:
            y = box[3] + 25 
    
        plt.imshow(img[:,:,::-1])
  
    output_image = img[:,:,::-1]
    st.image(output_image, caption='Predicted Image', use_column_width=True)

else:
    st.write('Please upload an image and a query image.')